Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An analysis of island models in evolutionary computation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A hybrid real-parameter genetic algorithm for function optimization
Advanced Engineering Informatics
A hybrid genetic algorithm with the Baldwin effect
Information Sciences: an International Journal
Parallel island-based multiobjectivised memetic algorithms for a 2D packing problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
An evolutionary algorithm derived from Charles Sanders Peirce's theory of universal evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
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One of the objectives of Evolutionary Computation (EC) has been to understand the processes of natural evolution and then model them algorithmically. Hans-Paul Schwefel, in his 1997 paper on the future challenges for EC argues that the more an algorithm models natural evolution at work in the universe, the better it will perform (even in terms of function optimization). There is enough data to suggest that slight differences in the understanding of the natural evolution can cause the associated Evolutionary Algorithms (EA) to change characteristically. The present paper tests Schwefel's hypothesis against Charles Sanders Peirce's theory which places semiotics, the theory of signs, at the heart of universal evolution. This course is followed because of three primary reasons. Firstly, Peirce has not been seriously tested in EC, although there have been EA based on other theories and sub-theories. Secondly, Peirce's universal theory, by not being restricted to biological evolution alone, qualifies for Schwefel's hypothesis, perhaps more than most other theories that have already been modeled algorithmically. But most importantly because, in experimental terms, it warrants an original claim that Peirce's insights are useful in improving the existing EA in computer science, as Peircean EA can potentially solve some of the major problems in this area such as the loss of diversity, stagnation, or premature convergence. This paper provides a novel framework and consequently a simple algorithm based on Peirce's theory of evolution, and tests it extensively against a benchmark set of mathematical problems of varying dimensions and complexity. Comparative results with classical and advanced EA form another significant part of the paper, and help in strengthening the viability of Schwefel-Peirce hypothesis for EC.